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Vergangene Naturkatastrophen zeigen, dass Katastrophenbewältigung ohne Berücksichtigung der Zivilbevölkerung nicht mehr denkbar ist. Sogenannte Spontanhelfende organisieren und koordinieren sich und ihre Hilfe dabei weitestgehend selbstständig. Ihr Zustrom an Einsatzorten hat in der Vergangenheit jedoch zu massiven Überlastungen von Einsatzorten geführt, wodurch offizielles Katastrophenpersonal vor Ort in der Bewältigung ihrer Arbeit durch diese zusätzliche Herausforderung behindert wurde. Die Probleme, die durch den Zustrom an Einsatzorten entstanden sind, waren vor allem auf die unvorhersehbare Natur der Spontanhelfenden und das Fehlen von Katastrophenmanagementwerkzeugen zur Planung und Vorbereitung solcher Szenarien zurückzuführen. Unter Verwendung eines designwissenschaftlichen Forschungsansatzes zielt die Dissertation darauf ab, ein Informationssystem zu entwerfen, zu entwickeln und zu evaluieren, das den Zustrom von Spontanhelfenden an Einsatzorten vorhersagen soll., ePast natural disasters showed that disaster management is no longer conceivable without considering civil participation, i.e., spontaneous volunteers, who predominantly organize and coordinate their help efforts on their own. In this regard, spontaneous volunteers led to, e.g., massive overloads of operating sites, causing official on-site disaster staff to be hampered in their work by this additional challenge. The problems caused by their influx at operating sites were primarily due to the unpredictable nature of spontaneous volunteers and a lack of disaster management tools to plan and train for such scenarios. Following a design science research approach, the dissertation aims to design, develop, and evaluate an information system for predicting the influx of spontaneous volunteers at operating sites. engl. Zsfassung

A new international facility for antiproton and ion research (FAIR) is currently under construction in Darmstadt, Germany. The high intensity primary beam required for different research experiments will be provided by the SIS100 heavy ion synchrotron. The synchrotron is composed of fast cycling superconducting magnets from which about 300 will be integrated in Quadrupole Doublet Modules (QDM). Each module consists of two units composed of a quadrupole and corrector magnets. The First of Series Quadrupole Doublet Module was delivered to the test facility at GSI in November 2019. The assembled doublet was subjected to a dedicated test program to verify the functionality of the module components at cryogenic temperature and operating conditions. The results obtained during the testing campaign will be presented., Proceedings of the 12th International Particle Accelerator Conference, IPAC2021, Campinas, SP, Brazil

The divisor class group of a hyperelliptic curve over a field is a finite abelian group at the center of many open questions in algebraic geometry and number theory. Sutherland surveys some of these, including the computation of the associated L-functions and zeta functions used in his investigation of Sato-Tate distributions. Many of these problems lend themselves to empirical investigation, and as emphasized by Sutherland, fast arithmetic in the divisor class group is crucial for their efficiency. Indeed, implementations of these fundamental operations are at the core of the algebraic geometry packages of widely-used computer algebra systems such as Magma and Sage. This thesis provides contributions to improve the efficiency of divisor class arithmetic on hyperelliptic curves, with special attention to the often ignored split model cases and to genus 2 and 3. There are two main contributions: the introduction of “Balanced NUCOMP” for improved arithmetic on curves given by split models of arbitrary genus, and improved explicit formulas for genus 2 (ramified and split models) and genus 3 (split models) based on Balanced NUCOMP for split models and NUCOMP for ramified models. Empirical analysis, using a complete Magma implementation and testing suite, is conducted with all contributed algorithms to provide proof of correctness and comparisons to previous best. Our results show that Balanced NUCOMP does offer improvements to split model arithmetic, further narrowing the performance gap between split and ramified models. Our explicit formulas require fewer field operations, most notably significantly fewer additions, than previous best for computing arithmetic on genus 2 (ramified and split models), and genus 3 (split models). Our empirical results demonstrate that our formulas yield the fastest general-purpose arithmetic for these cases.

Cognitive Radios (CRs) tackle the spectrum scarcity problem by allowing unlicensed access on already-licensed spectrum. Where primary user (PU) radio systems hold a privileged medium access, secondary users (SUs) try to utilize and use spare time-frequency resources to establish communication. At their core, CRs need to sense, detect and predict the medium access pattern of PUs in order to facilitate communication. A promising approach for inferring these predictions is the use of Machine Learning techniques and in particular Artifical Neural Networks (ANNs). ANNs try to learn a mapping from a set of inputs to a specific desired output and can therefore be directly applied to the problem of PU activity prediction. Especially Recurrent Neural Networks (RNNs) show adequate performance for noisy measurement data and prolonged training periods. In this work, the applicability of ANN-based channel state prediction is examined via a case study on the upcoming aeronautical communication technology L-band Digital Aeronautical Communications System (LDACS). LDACS is envisioned to reuse spectral resources currently primarily allocated to other aeronautical systems such as the Distance Measuring Equipment (DME) system. Primary and secondary users are modeled with respect to preliminary LDACS specifications and the performance of the proposed algorithms is evaluated via simulation. We show that RNNs can function as prediction agents for SU LDACS medium access and that stringent reliability and interference requirements can be met. A supervised learning problem, together with an incremental learning strategy is proposed to address time-varying PU channel access patterns. Finally, a brief discussion on the use of predictions in a distributed scheduling approach is given.

The Limited Relative Error algorithm is an alternative statistical method for data evaluation. Through online result analysis it continuously requests more samples until it deems the evaluation confident enough. With this it allows researchers to hand over the control of simulation time to the algorithm, and through a-priori configuration the target result resolution is set so that arbitrarily rare events can be investigated. We provide a new description of the method as well as a stand-alone implementation and an integration of the algorithm into the OMNeT++ simulator.